Industry datasets suitable for IFD can in some cases potentially be created, but it is difficult and costly to define high-quality labels that are accurately connected to relevant data.
Therefore, transfer learning [1]}, illustrated in Pipeline 2 in Figure REF , has become an increasingly popular approach to develop IFD methods without requiring a large labelled dataset in the target domain [2]}.
Ideally, a model could be developed/trained with data from a lab environment, then transferred to similar components in an industrial environment.
However, this remains a challenging goal due to differences between developing faults, heterogeneous environments, varying sensor and signal-to-noise conditions, and complex coupling of signal components.
Thus, a method for the extraction of labels for industry data would be valuable and can facilitate implementations of current IFD models, as well as transfer learning by providing access to labels in the target domain.
